Discrimination between Inrush and Internal Fault Currents in Power Transformers Using Hyperbolic S-Transform

Document Type : Original Article

Authors

1 Department of Electrical Technology, Institute Technical-Anbar, Middle Technical University, Baghdad, Iraq

2 Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran

Abstract

Numerous methods exist to distinguish between inrush current and internal faults, but these approaches have not yet become practical due to their inherent limitations. As a result, conventional methods, despite their well-known drawbacks, continue to be widely used in practice. In this paper, a new method based on time-frequency analysis is presented for detecting inrush current situations. To do this, a diverse array of scenarios involving a power transformer switching ON and internal fault cases are simulated using the PSCAD/EMTDC software package. Then, a hyperbolic S-transformer is employed to extract a determining index from the simulation results. Finally, a suitable threshold value for this index is computed so that inrush current can be distinguished from fault current by comparing the index with its threshold.  Evaluation of the efficiency of the proposed method using simulation and real data confirms its excellent accuracy. Therefore, it can be used in algorithms for power transformer differential protection to improve their stability during inrush current transients.

Keywords

Main Subjects

References

  1. Ebadi, A., Hosseini, S. and Abdoos, A., "A new restricted earth fault relay based on artificial intelligence", International Journal of Engineering, Transactions A: Basics, Vol. 32, No. 1, (2019), 62-70. doi: 10.5829/ije.2019.32.01a.08.
  2. Babaei-Roudi, F. and Abdoos, A., "Detection of internal fault from external fault and inrush current in power transformers based on combination of vmd and elm", Computational Intelligence in Electrical Engineering, Vol. 9, No. 1, (2018), 65-78. doi: 10.22108/ISEE.2018.110376.1115.
  3. Li, Z., Jiao, Z., He, A. and Xu, N., "A denoising-classification neural network for power transformer protection", Protection and Control of Modern Power Systems, Vol. 7, No. 1, (2022), 1-14. doi: 10.1186/s41601-022-00273-8.
  4. Ebadi, A., Hosseini, S. and Abdoos, A.A., "Designing of a new transformer ground differential relay based on probabilistic neural network", Engineering and Energy Management, Vol. 9, No. 4, (2023), 2-13. doi.
  5. Tajani, A.H.N., Bamshad, A. and Ghaffarzadeh, N., "A novel differential protection scheme for ac microgrids based on discrete wavelet transform", Electric Power Systems Research, Vol. 220, (2023), 109292. doi: 10.1016/j.epsr.2023.109292.
  6. Behvandi, A., Seifossadat, S.G. and Saffarian, A., "A new method for discrimination of internal fault from other transient states in power transformer using clarke’s transform and modified hyperbolic s-transform", Electric Power Systems Research, Vol. 178, (2020), 106023. doi: 10.1016/j.epsr.2019.106023.
  7. Khatib, T. and Arar, G., "Identification of power transformer currents by using random forest and boosting techniques", Mathematical Problems in Engineering, Vol. 2020, (2020), 1-12. doi: 10.1155/2020/1269367.
  8. Afrasiabi, S., Behdani, B., Afrasiabi, M., Mohammadi, M., Asheralieva, A. and Gheisari, M., "Differential protection of power transformers based on rslvq-gradient approach considering sfcl", in 2021 IEEE Madrid PowerTech, IEEE. (2021), 1-6.
  9. Jiao, W., Dong, K. and Zhao, J., "Intelligent transformer protection method based on convolutional neural network", in 2021 4th International Conference on Energy, Electrical and Power Engineering (CEEPE), IEEE. (2021), 698-703.
  10. Ebadi, A., Hosseini, S.M. and Abdoos, A.A., "A new time-frequency analysis based supervision method for the transformer restricted ground fault relay", International Journal of Electrical Power & Energy Systems, Vol. 129, (2021), 106858. doi: 10.1016/j.ijepes.2021.106858.
  11. Nandi, K., Das, A.K., Ghosh, R., Dalai, S. and Chatterjee, B., "Hyperbolic window s-transform aided deep neural network model-based power quality monitoring framework in electrical power system", IEEE Sensors Journal, Vol. 21, No. 12, (2021), 13695-13703. doi: 10.1109/JSEN.2021.3071935.
  12. Bkhaitawi, A., Abdoos, A.A. and Ebadi, A., "Presenting an adaptive restraint method to improve performance of ground differential protection of power transformer", International Journal of Engineering, Transactions B: Applications, Vol. 35, No. 11, (2022), 2213-2219. doi: 10.5829/IJE.2022.35.11B.16.
  13. Gorji, R.T., Hosseini, S.M., Abdoos, A.A. and Ebadi, A., "A hybrid intelligent method for compensation of current transformers saturation based on pso-svr", Periodica Polytechnica Electrical Engineering and Computer Science, Vol. 65, No. 1, (2021), 53-61. doi: 10.3311/PPee.16248.
  14. Ebadi, A., Hosseini, S.M. and Abdoos, A.A., "Immunity improvement of a low-impedance restricted earth fault relay by making its mechanism intelligent based on support vector machine", Computational Intelligence in Electrical Engineering, Vol. 9, No. 2, (2018), 29-40. doi: 10.22108/ISEE.2018.111464.1132.
  15. Yahiou, A., Mellah, H. and Bayadi, A., "Inrush current reduction by a point-on-wave energization strategy and sequential phase shifting in three-phase transformer", International Journal of Engineering, Transactions C: Aspects, Vol. 35, No. 12, (2022), 2321-2328. doi: 10.5829/IJE.2022.35.12C.07.
International Journal of Engineering
Volume 36, Issue 12
TRANSACTIONS C: Aspects
December 2023
Pages 2184-2189

Files

Share

How to cite

Statistics